omni-rag-mcp 3.0.1

General-purpose RAG MCP plugin — index anything, search with hybrid BM25+semantic, zero-config

omni-rag-mcp

A general-purpose RAG MCP plugin for token-efficient semantic search over any directory of files. Auto-ingests the current working directory on first search and provides hybrid search (BM25 + semantic), directory overview, structural analysis, and dependency graphs.

Zero-config by default: local Qdrant storage, ONNX embeddings, no external services required. Supports code, markdown, PDFs, CSVs, and more via pluggable extractors.

Quick Start

pip install omni-rag-mcp
omni-rag-setup

That's it. Restart Claude Code and the plugin auto-indexes your working directory on first search.

How It Works

Your Files  ->  Extractors  ->  Chunking  ->  Embedding  ->  Qdrant (local)
                                                                 |
Claude Code ->  MCP Tool Call  ->  Hybrid Search  ->  Relevant Snippets

1. First search auto-ingests your working directory (extracts content, chunks, generates embeddings, stores in local Qdrant)
2. Subsequent searches are fast hybrid lookups (BM25 + semantic) -- no re-ingestion needed
3. Incremental updates detect git changes and only re-embed modified files

MCP Tools

Tool	Purpose
search	Hybrid search over indexed files (auto-ingests if needed)
search_by_file	Search filtered by file path pattern
get_context	Compressed directory overview (languages, structure, dependencies)
get_file_signatures	Function/class signatures without reading every file
get_dependency_graph	Internal import/dependency graph
stats	Index size and configuration
ingest	Manual re-index (incremental by default, force=True for full)
check_status	Is the index current? Any uncommitted changes?

Embedding Providers

Zero-config by default. Choose your provider:

Provider	Config	Notes
ONNX (default)	None needed	Auto-downloads all-MiniLM-L6-v2 (23MB, 384-dim)
Ollama	OMNI_RAG_EMBEDDING_PROVIDER=ollama	Requires Ollama running with model pulled
OpenAI	OMNI_RAG_EMBEDDING_PROVIDER=openai + OMNI_RAG_OPENAI_API_KEY=sk-...	text-embedding-3-small
Voyage	OMNI_RAG_EMBEDDING_PROVIDER=voyage + OMNI_RAG_VOYAGE_API_KEY=...	voyage-code-3 (optimized for code)

Optional Extras

pip install omni-rag-mcp[pdf]    # PDF extraction (PyMuPDF)
pip install omni-rag-mcp[docx]   # Word document extraction
pip install omni-rag-mcp[image]  # Image/OCR extraction (Tesseract + Pillow)
pip install omni-rag-mcp[all]    # All optional extractors

Storage

By default, uses Qdrant in local/on-disk mode -- no Docker needed. Data stored in .omni-rag/ under your project directory.

For remote Qdrant:

OMNI_RAG_QDRANT_MODE=remote
OMNI_RAG_QDRANT_HOST=your-host
OMNI_RAG_QDRANT_PORT=6333

Configuration

All settings via environment variables with OMNI_RAG_ prefix. See config/.env.example for the full reference.

Legacy RAG_ prefix variables are still supported with deprecation warnings.

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
python -m pytest tests/ -v

# Health check
python scripts/health_check.py

Manual MCP Registration

If omni-rag-setup doesn't work, add this to your Claude Code MCP config:

{
  "mcpServers": {
    "omni-rag": {
      "command": "omni-rag"
    }
  }
}